Packages

library(plotly)
library(htmlwidgets) # for saving figures (with saveWidget function)
library(htmltools)

A/B Test

A/B Test - Definition

# Define minimum set of variables that fully define the A/B test results
t = 20     # Total number of subjects (A + B)
a = 10     # Number of subjects in group A
b = 10     # Number of subjects in group B
a_yes = 7  # Number of successes (yes) in group A
b_yes = 4  # Number of successes (yes) in group B

# Compute remaining A/B test results (using the variables above)
a_no = a - a_yes                # Number of failures (no) in group A
b_no = b - b_yes                # Number of failures (no) in group B
t_yes = a_yes + b_yes           # Total (A + B) successes (yes)
t_no  = a_no  + b_no            # Total (A + B) failures (no)
a_yes_pc = 100 * a_yes / a      # Percentage of successes (yes) in group A
b_yes_pc = 100 * b_yes / b      # Percentage of successes (yes) in group B
ab_yes_pc = a_yes_pc - b_yes_pc # Test statistic: Yes percentage diff. (A - B)

cat('Yes Rate (%):\n   A:', a_yes_pc, '\n   B:', b_yes_pc, '\n A-B:', ab_yes_pc)
Yes Rate (%):
   A: 70 
   B: 40 
 A-B: 30

A/B Test - Dataframe

set.seed(0) # For reproducible results

sub_rand = sample(1:t) # Subject randomization (for step 2)

# A/B results (for step 3): -1 = No, 0 = NA, +1 = Yes
ab_res = c(sample(rep(c(+1, -1), c(a_yes, a_no))),
           sample(rep(c(+1, -1), c(b_yes, b_no))))

# Data frame for animations
ab_df = data.frame(
  # A/B steps:   Step 1,    Step 2,    Step 3
  step    = c(rep(1, t), rep(2, t), rep(3, t)), # A/B step
  sub_y   = c(      1:t,       1:t,       1:t), # Subject y position
  sub_id  = c(      1:t,  sub_rand,  sub_rand), # Subject ID
  sub_col = c(rep(0, t), rep(0, t),    ab_res)  # Subject color (A/B result)
)

ab_df

A/B Test - Create Base Fig

A/B Test - Create Steps Fig

A/B Test - Save Steps Fig

saveWidget(ab_step_fig, "ab-steps-fig.html")

A/B Test - iframe

A/B Test - htmltools::tags$iframe

  • Bad: Does not show in the R notebook.
  • Good: Shows in the presentation.

A/B Test - htmltools::includeHTML

  • Bad: Does not show in the presentation
  • Good: Shows in the R notebook

A/B Test - Create Subjects Fig

A/B Test - Save Subjects Fig

saveWidget(ab_sub_fig, "ab-subjects-fig.html")

Permutation Test

Permutation Test - Dataframe

set.seed(0) # For reproducible results

sub_id_1 = c(which(ab_res == 1), which(ab_res == -1))
sub_col_1 = c(rep(1, t_yes), rep(-1, t_no))

perm_rand = sample(1:t)
sub_id_2 = sub_id_1[perm_rand]
sub_col_2 = sub_col_1[perm_rand]

# Data frame for animations
perm_df = data.frame(
  # Perm steps:  Step 0,    Step 1,    Step 2
  step    = c(rep(0, t), rep(1, t), rep(2, t)), # Permutation step
  sub_y   = c(      1:t,       1:t,       1:t), # Subject y position
  sub_id  = c(      1:t,  sub_id_1,  sub_id_2), # Subject ID
  sub_col = c(   ab_res, sub_col_1, sub_col_2)  # Subject color (A/B result)
)

perm_df

Permutation Test - Create Base Fig

shapes = list(
  list(type="rect", x0=-0.2, x1=0.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=-0.2, x1=0.2, y0=10.5,  y1=20.5),
  list(type="rect", x0=0.8, x1=1.2, y0=0.5, y1=20.5),
  list(type="rect", x0=1.8, x1=2.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=1.8, x1=2.2, y0=10.5, y1=20.5)
)

annotations = list(
  list(x=-0.8, y=5.5, text="A: 70%", showarrow=FALSE, font=list(size=18)),
  list(x=-0.8, y=15.5, text="B: 40%", showarrow=FALSE, font=list(size=18)),
  list(x=-0.8, y=10.5, text="A-B: 30%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=5.5, text="A: 50%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=15.5, text="B: 60%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=10.5, text="A-B: -10%", showarrow=FALSE, font=list(size=18))
)

x_labels = list("Step 0\nA/B Results", 'Step 1\n"Bag"', "Steps 2-5\nOne Permutation")
x_axis = list(title="", range = c(-1.3, 3.4), tickvals = list(0, 1, 2),
              ticktext = x_labels, zeroline=FALSE, showgrid=FALSE)
y_axis = list(title="", range = c(0, 21), zeroline=FALSE, showline=FALSE,
              showticklabels=FALSE, showgrid = FALSE)

perm_base_fig = perm_df %>%
  plot_ly(x=~step, y=~sub_y, color=~sub_col, colors=c("red", "black", "green3")) %>%
  add_trace(type="scatter", mode="markers", marker=list(size=10), opacity=0.3) %>%
  config(displayModeBar=FALSE) %>%
  layout(annotations=annotations, margin=list(l=0, r=0, b=0, t=0, pad=0), 
         shapes=shapes, xaxis=x_axis, yaxis=y_axis)

perm_base_fig

Permutation Test - Create Steps Fig

perm_step_fig = perm_base_fig %>%
  add_trace(type="scatter", mode="markers", marker=list(size=10), frame=~step, ids=~sub_id) %>%
  hide_colorbar() %>%
  layout(showlegend=FALSE)

perm_step_fig

Permutatio Test - Save Steps Fig

saveWidget(perm_step_fig, "permutation-steps-fig.html")

Permutation Test - Create Subjects Fig

perm_sub_fig = perm_base_fig %>%
  add_trace(type="scatter", mode="markers+lines", marker=list(size=10), frame=~sub_id) %>%
  hide_colorbar() %>%
  layout(showlegend=FALSE)

perm_sub_fig

Permutatio Test - Save Subjects Fig

saveWidget(perm_sub_fig, "permutation-subjects-fig.html")
---
title: "Diagrams"
output: html_notebook
---

```{r, include=FALSE}
#knitr::opts_chunk$set(echo = FALSE)
```

## Packages

```{r, message=FALSE}
library(plotly)
library(htmlwidgets) # for saving figures (with saveWidget function)
library(htmltools)
```

# A/B Test

## A/B Test - Definition {.smaller}

```{r}
# Define minimum set of variables that fully define the A/B test results
t = 20     # Total number of subjects (A + B)
a = 10     # Number of subjects in group A
b = 10     # Number of subjects in group B
a_yes = 7  # Number of successes (yes) in group A
b_yes = 4  # Number of successes (yes) in group B

# Compute remaining A/B test results (using the variables above)
a_no = a - a_yes                # Number of failures (no) in group A
b_no = b - b_yes                # Number of failures (no) in group B
t_yes = a_yes + b_yes           # Total (A + B) successes (yes)
t_no  = a_no  + b_no            # Total (A + B) failures (no)
a_yes_pc = 100 * a_yes / a      # Percentage of successes (yes) in group A
b_yes_pc = 100 * b_yes / b      # Percentage of successes (yes) in group B
ab_yes_pc = a_yes_pc - b_yes_pc # Test statistic: Yes percentage diff. (A - B)

cat('Yes Rate (%):\n   A:', a_yes_pc, '\n   B:', b_yes_pc, '\n A-B:', ab_yes_pc)
```

## A/B Test - Dataframe {.smaller}

```{r}
set.seed(0) # For reproducible results

sub_rand = sample(1:t) # Subject randomization (for step 2)

# A/B results (for step 3): -1 = No, 0 = NA, +1 = Yes
ab_res = c(sample(rep(c(+1, -1), c(a_yes, a_no))),
           sample(rep(c(+1, -1), c(b_yes, b_no))))

# Data frame for animations
ab_df = data.frame(
  # A/B steps:   Step 1,    Step 2,    Step 3
  step    = c(rep(1, t), rep(2, t), rep(3, t)), # A/B step
  sub_y   = c(      1:t,       1:t,       1:t), # Subject y position
  sub_id  = c(      1:t,  sub_rand,  sub_rand), # Subject ID
  sub_col = c(rep(0, t), rep(0, t),    ab_res)  # Subject color (A/B result)
)

ab_df
```

## A/B Test - Create Base Fig

```{r, echo=FALSE, message=FALSE, warning=FALSE}
shapes = list(
  list(type="rect", x0=0.8, x1=1.2, y0=0.5,  y1=20.5),
  list(type="rect", x0=1.8, x1=2.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=1.8, x1=2.2, y0=10.5, y1=20.5),
  list(type="rect", x0=2.8, x1=3.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=2.8, x1=3.2, y0=10.5, y1=20.5),
  list(type="rect", x0=2.2, x1=2.8, y0=0.5,  y1=10.5, fillcolor="blue", opacity=0.2),
  list(type="rect", x0=2.2, x1=2.8, y0=10.5, y1=20.5, fillcolor="yellow", opacity=0.2)
)

annotations = list(
  list(x=2.5, y=5.5, text="A", showarrow=FALSE, font=list(size=20)),
  list(x=2.5, y=15.5, text="B", showarrow=FALSE, font=list(size=20)),
  list(x=3.6, y=5.5, text="A: 70%", showarrow=FALSE, font=list(size=18)),
  list(x=3.6, y=15.5, text="B: 40%", showarrow=FALSE, font=list(size=18)),
  list(x=3.6, y=10.5, text="A-B: 30%", showarrow=FALSE, font=list(size=18))
)

x_labels = list("Step 1\nAll subjects", "Step 2\nRandomization", "Step 3\nResults")
x_axis = list(title="", range = c(0.5, 4.2), tickvals = list(1, 2, 3),
              ticktext = x_labels)
y_axis = list(title="", range = c(0, 21), zeroline=FALSE, showline=FALSE,
              showticklabels=FALSE, showgrid = FALSE)

ab_base_fig = ab_df %>%
  plot_ly(x=~step, y=~sub_y, color=~sub_col, colors=c("red", "black", "green3")) %>%
  add_trace(marker=list(size=10), opacity=0.3) %>%
  config(displayModeBar=FALSE) %>%
  layout(annotations=annotations, margin=list(l=0, r=0, b=0, t=0, pad=0), 
         shapes=shapes, xaxis=x_axis, yaxis=y_axis)

ab_base_fig
```

## A/B Test - Create Steps Fig

```{r, echo=FALSE, message=FALSE, warning=FALSE}
# (r chunk option: fig.height=5.5)
ab_step_fig = ab_base_fig %>%
  add_trace(marker=list(size=10), frame=~step, ids=~sub_id) %>%
  hide_colorbar() %>%
  layout(showlegend=FALSE)

ab_step_fig
```

## A/B Test - Save Steps Fig

```{r}
saveWidget(ab_step_fig, "ab-steps-fig.html")
```

## A/B Test - `iframe`

<iframe src="ab-steps-fig.html"></iframe>

<!--
<iframe src="ab-steps-fig.html" style="border: none;"></iframe>
-->

## A/B Test - `htmltools::tags$iframe`

- Bad: Does not show in the R notebook.
- Good: Shows in the presentation.

```{r, echo=FALSE}
#file_path = "ab-steps-fig.html"
#htmltools::tags$iframe(src = file_path, scrolling = "no", 
#                       seamless = "seamless", frameBorder = "0")
```

## A/B Test - `htmltools::includeHTML`

- Bad: Does not show in the presentation
- Good: Shows in the R notebook

```{r, echo=FALSE}
#htmltools::includeHTML("ab-steps-fig.html")
```

## A/B Test - Create Subjects Fig

```{r, echo=FALSE, message=FALSE, warning=FALSE}
ab_sub_fig = ab_base_fig %>%
  add_trace(mode="markers+lines", marker=list(size=10), frame=~sub_id,
            line=list(color="gray")) %>% # dash="dot"
  hide_colorbar() %>%
  layout(showlegend=FALSE)

ab_sub_fig
```

## A/B Test - Save Subjects Fig

```{r}
saveWidget(ab_sub_fig, "ab-subjects-fig.html")
```

# Permutation Test

## Permutation Test - Dataframe {.smaller}

```{r}
set.seed(0) # For reproducible results

sub_id_1 = c(which(ab_res == 1), which(ab_res == -1))
sub_col_1 = c(rep(1, t_yes), rep(-1, t_no))

perm_rand = sample(1:t)
sub_id_2 = sub_id_1[perm_rand]
sub_col_2 = sub_col_1[perm_rand]

# Data frame for animations
perm_df = data.frame(
  # Perm steps:  Step 0,    Step 1,    Step 2
  step    = c(rep(0, t), rep(1, t), rep(2, t)), # Permutation step
  sub_y   = c(      1:t,       1:t,       1:t), # Subject y position
  sub_id  = c(      1:t,  sub_id_1,  sub_id_2), # Subject ID
  sub_col = c(   ab_res, sub_col_1, sub_col_2)  # Subject color (A/B result)
)

perm_df
```

## Permutation Test - Create Base Fig

```{r}
shapes = list(
  list(type="rect", x0=-0.2, x1=0.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=-0.2, x1=0.2, y0=10.5,  y1=20.5),
  list(type="rect", x0=0.8, x1=1.2, y0=0.5, y1=20.5),
  list(type="rect", x0=1.8, x1=2.2, y0=0.5,  y1=10.5),
  list(type="rect", x0=1.8, x1=2.2, y0=10.5, y1=20.5)
)

annotations = list(
  list(x=-0.8, y=5.5, text="A: 70%", showarrow=FALSE, font=list(size=18)),
  list(x=-0.8, y=15.5, text="B: 40%", showarrow=FALSE, font=list(size=18)),
  list(x=-0.8, y=10.5, text="A-B: 30%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=5.5, text="A: 50%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=15.5, text="B: 60%", showarrow=FALSE, font=list(size=18)),
  list(x=2.8, y=10.5, text="A-B: -10%", showarrow=FALSE, font=list(size=18))
)

x_labels = list("Step 0\nA/B Results", 'Step 1\n"Bag"', "Steps 2-5\nOne Permutation")
x_axis = list(title="", range = c(-1.3, 3.4), tickvals = list(0, 1, 2),
              ticktext = x_labels, zeroline=FALSE, showgrid=FALSE)
y_axis = list(title="", range = c(0, 21), zeroline=FALSE, showline=FALSE,
              showticklabels=FALSE, showgrid = FALSE)

perm_base_fig = perm_df %>%
  plot_ly(x=~step, y=~sub_y, color=~sub_col, colors=c("red", "black", "green3")) %>%
  add_trace(type="scatter", mode="markers", marker=list(size=10), opacity=0.3) %>%
  config(displayModeBar=FALSE) %>%
  layout(annotations=annotations, margin=list(l=0, r=0, b=0, t=0, pad=0), 
         shapes=shapes, xaxis=x_axis, yaxis=y_axis)

perm_base_fig
```

## Permutation Test - Create Steps Fig

```{r}
perm_step_fig = perm_base_fig %>%
  add_trace(type="scatter", mode="markers", marker=list(size=10), frame=~step, ids=~sub_id) %>%
  hide_colorbar() %>%
  layout(showlegend=FALSE)

perm_step_fig
```

## Permutatio Test - Save Steps Fig

```{r}
saveWidget(perm_step_fig, "permutation-steps-fig.html")
```

## Permutation Test - Create Subjects Fig

```{r}
perm_sub_fig = perm_base_fig %>%
  add_trace(type="scatter", mode="markers+lines", marker=list(size=10), frame=~sub_id) %>%
  hide_colorbar() %>%
  layout(showlegend=FALSE)

perm_sub_fig
```

## Permutatio Test - Save Subjects Fig

```{r}
saveWidget(perm_sub_fig, "permutation-subjects-fig.html")
```

